A UNet-like Transformer Network for Camouflaged Object Detection

The role of Camouflaged Object Detection (COD) is to identify the objects that integrate seamlessly with the surrounding environment. Due to the high intrinsic similarity between the objects and their background, this task presents greater challenges than traditional object detection. Most existing COD methods often have a large number of parameters and high computational complexity in the pursuit of detection accuracy, which hinders the application of COD in practical scenarios. To address this issue, we propose a UNet-like Transformer Network for COD, termed UTNet, which achieves competitive detection accuracy with a smaller parameter set. Specifically, we propose a Camouflaged Region Awareness Module (CRAM) consisting of a Hierarchical Attention Mechanism (HAM) that groups features to reveal intrinsic consistency between sub-features. This CRAM can be embedded into the backbone network, giving it powerful modeling capabilities. And, we present a Contextual Knowledge Collector (CKC) that exploits a cross-aggregation approach for neighboring feature layers, promoting the flow of semantic information from high-level to low-level features, and ensuring the integrity of camouflaged objects at each level of features. Furthermore, we introduce a progressive decoder that utilizes a cascade of attention units to filter noise and explores knowledge aggregation to emphasize features from different levels, ensuring that camouflaged objects have complete spatial details at the local level. Extensive experimental results show that UTNet achieves competitive results compared to 20 state-of-the-art methods. Codes and results are released on https://github.com/hjy0518/UTNet.